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title = {{{BRITS}}: {{Bidirectional Recurrent Imputation}} for {{Time Series}}},
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year = {2018},
month = may,
journal = {arXiv:1805.10572 [cs, stat]},
eprint = {1805.10572},
eprinttype = {arxiv},
primaryclass = {cs, stat},
url = {http://arxiv.org/abs/1805.10572},
archiveprefix = {arXiv},
keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
}

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author={Yoon, Jinsung and Zame, William R. and van der Schaar, Mihaela},
journal={IEEE Transactions on Biomedical Engineering},
title={Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks},
year={2019},
volume={66},
number={5},
pages={1477-1490},
doi={10.1109/TBME.2018.2874712}
}

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title = {Recurrent {{Neural Networks}} for {{Multivariate Time Series}} with {{Missing Values}}},
author = {Che, Zhengping and Purushotham, Sanjay and Cho, Kyunghyun and Sontag, David and Liu, Yan},
year = {2018},
month = apr,
journal = {Scientific Reports},
volume = {8},
number = {1},
pages = {6085},
publisher = {{Nature Publishing Group}},
issn = {2045-2322},
doi = {10.1038/s41598-018-24271-9},
url = {https://www.nature.com/articles/s41598-018-24271-9},
copyright = {2018 The Author(s)}
}

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title = {Bayesian {{Temporal Factorization}} for {{Multidimensional Time Series Prediction}}},
author = {Chen, Xinyu and Sun, Lijun},
year = {2021},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
eprint = {1910.06366},
eprinttype = {arxiv},
pages = {1--1},
issn = {0162-8828, 2160-9292, 1939-3539},
doi = {10.1109/TPAMI.2021.3066551},
url = {http://arxiv.org/abs/1910.06366},
archiveprefix = {arXiv},
keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
}

@article{choi2020RDISRandom,
title = {{{RDIS}}: {{Random Drop Imputation}} with {{Self-Training}} for {{Incomplete Time Series Data}}},
author = {Choi, Tae-Min and Kang, Ji-Su and Kim, Jong-Hwan},
year = {2020},
month = oct,
journal = {arXiv:2010.10075 [cs, stat]},
eprint = {2010.10075},
eprinttype = {arxiv},
primaryclass = {cs, stat},
url = {http://arxiv.org/abs/2010.10075},
archiveprefix = {arXiv},
keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
}

@article{cini2021MultivariateTime,
title = {Multivariate {{Time Series Imputation}} by {{Graph Neural Networks}}},
author = {Cini, Andrea and Marisca, Ivan and Alippi, Cesare},
year = {2021},
month = sep,
journal = {arXiv:2108.00298 [cs]},
eprint = {2108.00298},
eprinttype = {arxiv},
primaryclass = {cs},
url = {http://arxiv.org/abs/2108.00298},
archiveprefix = {arXiv},
keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning}
}

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title = {Missing {{Data Imputation}} via {{Denoising Autoencoders}}: {{The Untold Story}}},
booktitle = {Advances in {{Intelligent Data Analysis XVII}}},
author = {Costa, Adriana Fonseca and Santos, Miriam Seoane and Soares, Jastin Pompeu and Abreu, Pedro Henriques},
editor = {Duivesteijn, Wouter and Siebes, Arno and Ukkonen, Antti},
year = {2018},
series = {Lecture {{Notes}} in {{Computer Science}}},
pages = {87--98},
publisher = {{Springer International Publishing}},
address = {{Cham}},
doi = {10.1007/978-3-030-01768-2_8},
isbn = {978-3-030-01768-2},
keywords = {Data imputation,Denoising autoencoders,Missing data,Missing mechanisms}
}

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title = {Deep Learning for Clustering of Multivariate Clinical Patient Trajectories with Missing Values},
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year = {2019},
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journal = {GigaScience},
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number = {11},
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doi = {10.1093/gigascience/giz134},
url = {https://doi.org/10.1093/gigascience/giz134}
}

@article{du2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
pages = {119619},
year = {2023},
issn = {0957-4174},
doi = {10.1016/j.eswa.2023.119619},
url = {https://arxiv.org/abs/2202.08516},
author = {Wenjie Du and David Cote and Yan Liu},
}

@inproceedings{fortuin2020gpvae,
title={{GP-VAE: Deep probabilistic time series imputation}},
author={Fortuin, Vincent and Baranchuk, Dmitry and R{\"a}tsch, Gunnar and Mandt, Stephan},
booktitle={International conference on artificial intelligence and statistics},
pages={1651--1661},
year={2020},
organization={PMLR}
}

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title = {Set {{Functions}} for {{Time Series}}},
author = {Horn, Max and Moor, Michael and Bock, Christian and Rieck, Bastian and Borgwardt, Karsten},
year = {2019},
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url = {https://arxiv.org/abs/1909.12064v3}
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title = {Comparing Partitions},
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issn = {1432-1343},
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url = {https://doi.org/10.1007/BF01908075},
keywords = {Consensus indices,Measures of agreement,Measures of association}
}

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@inproceedings{liu2019NAOMI,
title = {{{NAOMI}}: {{Non-Autoregressive Multiresolution Sequence Imputation}}},
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author = {Liu, Yukai and Yu, Rose and Zheng, Stephan and Zhan, Eric and Yue, Yisong},
year = {2019},
month = oct,
eprint = {1901.10946},
eprinttype = {arxiv},
primaryclass = {cs, stat},
url = {http://arxiv.org/abs/1901.10946},
archiveprefix = {arXiv},
keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
}

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title = {Multivariate {{Time Series Imputation}} with {{Generative Adversarial Networks}}},
booktitle = {Advances in {{Neural Information Processing Systems}} 31},
author = {Luo, Yonghong and Cai, Xiangrui and ZHANG, Ying and Xu, Jun and {xiaojie}, Yuan},
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year = {2018},
pages = {1596--1607},
publisher = {{Curran Associates, Inc.}},
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}

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title = {{{CDSA}}: {{Cross-Dimensional Self-Attention}} for {{Multivariate}}, {{Geo-tagged Time Series Imputation}}},
author = {Ma, Jiawei and Shou, Zheng and Zareian, Alireza and Mansour, Hassan and Vetro, Anthony and Chang, Shih-Fu},
year = {2019},
month = aug,
journal = {arXiv:1905.09904 [cs, stat]},
eprint = {1905.09904},
eprinttype = {arxiv},
primaryclass = {cs, stat},
url = {http://arxiv.org/abs/1905.09904},
archiveprefix = {arXiv},
keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
}

@article{ma2021CRLI,
title = {Learning {{Representations}} for {{Incomplete Time Series Clustering}}},
author = {Ma, Qianli and Chen, Chuxin and Li, Sen and Cottrell, Garrison W.},
year = {2021},
month = may,
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
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issn = {2374-3468},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17070},
copyright = {Copyright (c) 2021 Association for the Advancement of Artificial Intelligence},
keywords = {Time-Series/Data Streams}
}

@article{miao2021SSGAN,
title = {Generative {{Semi-supervised Learning}} for {{Multivariate Time Series Imputation}}},
author = {Miao, Xiaoye and Wu, Yangyang and Wang, Jun and Gao, Yunjun and Mao, Xudong and Yin, Jianwei},
year = {2021},
month = may,
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
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number = {10},
pages = {8983--8991},
issn = {2374-3468},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17086},
copyright = {Copyright (c) 2021 Association for the Advancement of Artificial Intelligence},
keywords = {Time-Series/Data Streams}
}

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title = {Time {{Series Cluster Kernel}} for {{Learning Similarities}} between {{Multivariate Time Series}} with {{Missing Data}}},
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year = {2017},
month = jun,
journal = {arXiv:1704.00794 [cs, stat]},
eprint = {1704.00794},
eprinttype = {arxiv},
primaryclass = {cs, stat},
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keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
}

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title = {{{STING}}: {{Self-attention}} Based {{Time-series Imputation Networks}} Using {{GAN}}},
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author = {Oh, Eunkyu and Kim, Taehun and Ji, Yunhu and Khyalia, Sushil},
year = {2021},
month = dec,
pages = {1264--1269},
issn = {2374-8486},
doi = {10.1109/ICDM51629.2021.00155},
keywords = {bidirectional RNN,Conferences,Correlation,Data collection,Deep learning,generative adversarial networks,Generative adversarial networks,Recurrent neural networks,self-attention,Time series analysis,time-series imputation}
}

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year = {2021},
month = jul,
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keywords = {Informative missingness,Kernel methods,Missing data,Multivariate time series,Semi-supervised learning}
}

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pages = {798--807},
doi = {10.1109/BigData50022.2020.9378408},
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}

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year={2022},
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@inproceedings{wu2023timesnet,
title={{TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis}},
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@inproceedings{zhang2023crossformer,
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title={{iTransformer: Inverted Transformers Are Effective for Time Series Forecasting}},
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booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
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publisher = {Curran Associates, Inc.},
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pages = {12271--12290},
publisher = {Curran Associates, Inc.},
title = {Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors},
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url={https://openreview.net/forum?id=rkgNKkHtvB}
}

@article{das2023tide,
title={Long-term Forecasting with Ti{DE}: Time-series Dense Encoder},
author={Abhimanyu Das and Weihao Kong and Andrew Leach and Shaan K Mathur and Rajat Sen and Rose Yu},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=pCbC3aQB5W},
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@inproceedings{chen2023contiformer,
title={ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling},
author={Yuqi Chen and Kan Ren and Yansen Wang and Yuchen Fang and Weiwei Sun and Dongsheng Li},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YJDz4F2AZu}
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@inproceedings{lee2024pits,
title={Learning to Embed Time Series Patches Independently},
author={Seunghan Lee and Taeyoung Park and Kibok Lee},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=WS7GuBDFa2}
}

@inproceedings{wang2023micn,
title={{MICN}: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting},
author={Huiqiang Wang and Jian Peng and Feihu Huang and Jince Wang and Junhui Chen and Yifei Xiao},
booktitle={The Eleventh International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=zt53IDUR1U}
}

@inproceedings{wang2024timemixer,
title={TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting},
author={Shiyu Wang and Haixu Wu and Xiaoming Shi and Tengge Hu and Huakun Luo and Lintao Ma and James Y. Zhang and JUN ZHOU},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=7oLshfEIC2}
}

@article{gu2023mamba,
title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces},
author={Gu, Albert and Dao, Tri},
journal={arXiv preprint arXiv:2312.00752},
year={2023}
}

@article{zhang2022lightts,
title={Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures},
author={Tianping Zhang and Yizhuo Zhang and Wei Cao and Jiang Bian and Xiaohan Yi and Shun Zheng and Jian Li},
year={2022},
eprint={2207.01186},
archivePrefix={arXiv},
primaryClass={cs.LG}
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@article{lin2023segrnn,
title={{SegRNN}: Segment Recurrent Neural Network for Long-Term Time Series Forecasting},
author={Shengsheng Lin and Weiwei Lin and Wentai Wu and Feiyu Zhao and Ruichao Mo and Haotong Zhang},
year={2023},
eprint={2308.11200},
archivePrefix={arXiv},
primaryClass={cs.LG}
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@article{chen2023tsmixer,
title={{TSMixer}: An All-MLP Architecture for Time Series Forecasting},
author={Si-An Chen and Chun-Liang Li and Nate Yoder and Sercan O. Arik and Tomas Pfister},
year={2023},
eprint={2303.06053},
archivePrefix={arXiv},
primaryClass={cs.LG}
}

@inproceedings{choi2024timecib,
title={Conditional Information Bottleneck Approach for Time Series Imputation},
author={MinGyu Choi and Changhee Lee},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=K1mcPiDdOJ}
}

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title={{UniTS}: Building a Unified Time Series Model},
author={Gao, Shanghua and Koker, Teddy and Queen, Owen and Hartvigsen, Thomas and Tsiligkaridis, Theodoros and Zitnik, Marinka},
journal={arXiv},
url={https://arxiv.org/pdf/2403.00131.pdf},
year={2024}
}

@article{liu2024timesurl,
title={{TimesURL}: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning},
author={Liu, Jiexi and Chen, Songcan},
volume={38},
url={https://ojs.aaai.org/index.php/AAAI/article/view/29299},
DOI={10.1609/aaai.v38i12.29299},
number={12},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2024},
month={Mar.},
pages={13918-13926},
}

@inproceedings{luo2024moderntcn,
title={Modern{TCN}: A Modern Pure Convolution Structure for General Time Series Analysis},
author={Luo Donghao, Wue Xue},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=vpJMJerXHU}
}

@inproceedings{liu2022scinet,
author = {LIU, Minhao and Zeng, Ailing and Chen, Muxi and Xu, Zhijian and LAI, Qiuxia and Ma, Lingna and Xu, Qiang},
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editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
pages = {5816--5828},
publisher = {Curran Associates, Inc.},
title = {SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction},
url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/266983d0949aed78a16fa4782237dea7-Paper-Conference.pdf},
volume = {35},
year = {2022}
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@inproceedings{kim2022revin,
title={Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift},
author={Taesung Kim and Jinhee Kim and Yunwon Tae and Cheonbok Park and Jang-Ho Choi and Jaegul Choo},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=cGDAkQo1C0p}
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editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {17766--17778},
publisher = {Curran Associates, Inc.},
title = {Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/cdf6581cb7aca4b7e19ef136c6e601a5-Paper.pdf},
volume = {33},
year = {2020}
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title={{FITS}: Modeling Time Series with \$10k\$ Parameters},
author={Zhijian Xu and Ailing Zeng and Qiang Xu},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=bWcnvZ3qMb}
}

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title={ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation},
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booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
publisher = {Association for Computing Machinery},
year={2024},
series = {KDD '24},
doi = {10.1145/3637528.3671751},
url = {https://doi.org/10.1145/3637528.3671751},
}

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author={Zhan, Tianxiang and He, Yuanpeng and Deng, Yong and Li, Zhen and Du, Wenjie and Wen, Qingsong},
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title={Time Evidence Fusion Network: Multi-Source View in Long-Term Time Series Forecasting},
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title={Time-{LLM}: Time Series Forecasting by Reprogramming Large Language Models},
author={Ming Jin and Shiyu Wang and Lintao Ma and Zhixuan Chu and James Y. Zhang and Xiaoming Shi and Pin-Yu Chen and Yuxuan Liang and Yuan-Fang Li and Shirui Pan and Qingsong Wen},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Unb5CVPtae}
}

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title={Knowledge Enhanced Conditional Imputation for Healthcare Time-series},
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journal={arXiv preprint arXiv:2312.16713},
year={2023}
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@inproceedings{zhou2023gpt4ts,
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booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=gMS6FVZvmF}
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title={TS2Vec: Towards Universal Representation of Time Series},
volume={36},
url={https://ojs.aaai.org/index.php/AAAI/article/view/20881},
DOI={10.1609/aaai.v36i8.20881},
number={8},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Yue, Zhihan and Wang, Yujing and Duan, Juanyong and Yang, Tianmeng and Huang, Congrui and Tong, Yunhai and Xu, Bixiong},
year={2022},
month={Jun.},
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@InProceedings{goswami2024moment,
title = {{MOMENT}: A Family of Open Time-series Foundation Models},
author = {Goswami, Mononito and Szafer, Konrad and Choudhry, Arjun and Cai, Yifu and Li, Shuo and Dubrawski, Artur},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {16115--16152},
year = {2024},
editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
volume = {235},
series = {Proceedings of Machine Learning Research},
month = {21--27 Jul},
publisher =    {PMLR},
pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/goswami24a/goswami24a.pdf},
url = {https://proceedings.mlr.press/v235/goswami24a.html},
}

@inproceedings{wang2025timemixerpp,
title={{TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis}},
author={Shiyu Wang and Jiawei LI and Xiaoming Shi and Zhou Ye and Baichuan Mo and Wenze Lin and Ju Shengtong and Zhixuan Chu and Ming Jin},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=1CLzLXSFNn}
}

@article{talukder2024totem,
title={{TOTEM}: {TO}kenized Time Series {EM}beddings for General Time Series Analysis},
author={Sabera J Talukder and Yisong Yue and Georgia Gkioxari},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2024},
url={https://openreview.net/forum?id=QlTLkH6xRC},
note={}
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@InProceedings{eldele2024tslanet,
title = {{TSLAN}et: Rethinking Transformers for Time Series Representation Learning},
author = {Eldele, Emadeldeen and Ragab, Mohamed and Chen, Zhenghua and Wu, Min and Li, Xiaoli},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {12409--12428},
year = {2024},
editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
volume = {235},
series = {Proceedings of Machine Learning Research},
month = {21--27 Jul},
publisher = {PMLR},
pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/eldele24a/eldele24a.pdf},
url = {https://proceedings.mlr.press/v235/eldele24a.html},
}
